Cerisier is the first mechanized program logic for modular reasoning about trusted, untrusted, and attested code in capability machines, with a universal contract for untrusted code and demonstrations on secure computation and mutual attestation.
In: Proceed- ings of the 29th ACM International Conference on Architectural Support for Pro- gramming Languages and Operating Systems, Volume 2, pp
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FlexiFlow optimizes carbon footprint for item-level intelligence on flexible electronics by modeling lifetime variation, delivering 1.62X microarchitectural and 14.5X algorithmic reductions plus a 30.9 kHz tape-out.
PoisonCap uses a new poison capability format to deliver strict use-after-free and initialization safety for CHERI systems with no fundamental overhead over Cornucopia baselines.
ShuntServe reports 1.42x and 1.35x higher throughput than baselines plus 31.9 percent and 31.2 percent cost-efficiency gains over on-demand instances for Llama-3.1-70B and Qwen3-32B on heterogeneous AWS spot clusters.
A method using shared-memory occupancy shaping and elevated communication priority achieves up to 25.5% faster multi-GPU ML execution on NVIDIA and AMD GPUs.
citing papers explorer
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Cerisier: A Program Logic for Attestation in a Capability Machine
Cerisier is the first mechanized program logic for modular reasoning about trusted, untrusted, and attested code in capability machines, with a universal contract for untrusted code and demonstrations on secure computation and mutual attestation.
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Lifetime-Aware Design for Item-Level Intelligence at the Extreme Edge
FlexiFlow optimizes carbon footprint for item-level intelligence on flexible electronics by modeling lifetime variation, delivering 1.62X microarchitectural and 14.5X algorithmic reductions plus a 30.9 kHz tape-out.
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PoisonCap: Efficient Hierarchical Temporal Safety for CHERI
PoisonCap uses a new poison capability format to deliver strict use-after-free and initialization safety for CHERI systems with no fundamental overhead over Cornucopia baselines.
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ShuntServe: Cost-Efficient LLM Serving on Heterogeneous Spot GPU Clusters
ShuntServe reports 1.42x and 1.35x higher throughput than baselines plus 31.9 percent and 31.2 percent cost-efficiency gains over on-demand instances for Llama-3.1-70B and Qwen3-32B on heterogeneous AWS spot clusters.
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Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads
A method using shared-memory occupancy shaping and elevated communication priority achieves up to 25.5% faster multi-GPU ML execution on NVIDIA and AMD GPUs.
- Ding-Dong Ditch: Peeking Into Spot Instance Availability